The following discussion illustrates a project that is well suited to the capabilities of an independent consultant in the Umbrex Biotechnology Practice. This is an illustrative example. Umbrex consultants adapt their methodology, timeline, and deliverables to the specific needs of each client.
1) Client Situation
The client operated within the therapeutics-focused biotech ecosystem and required support with RWE And Clinical AI For Trial Acceleration in the context of Biotechnology. Leaders across Emerging Therapeutic Biotech (Preclinical), Clinical-Stage Biotech (Phase I–III), Commercial Biotech (Post-Approval), Platform Biotech (Therapeutic Discovery), and Biotech Investors & Incubators sought to build real-world evidence (RWE) pipelines and machine learning to accelerate enrollment via patient finding, eligibility inference, and site selection, improving protocol feasibility and startup timelines. The diagnostic surfaced concrete pain points:
- Fragmented and delayed data access
- EHR data lived in heterogeneous schemas across health systems; structured fields (diagnoses, labs, medications, procedures) were incomplete; unstructured notes contained critical signals (disease activity, performance status, contraindications) not captured by existing SQL-based feasibility queries.
- Claims datasets provided longitudinal coverage but lacked clinical nuance; linkage between EHR and claims was inconsistent, and tokenization across partners was not standardized.
- Genomics, imaging, and central lab feeds were siloed; social determinants (SDoH) and distance-to-site data were absent from feasibility models.
- Eligibility criteria not computable
- Inclusion/exclusion criteria were authored as narrative text; there was no systematic pipeline to translate criteria into computable phenotypes using value sets (ICD-10/SNOMED, LOINC, RxNorm, CPT/HCPCS) and NLP-derived concepts (e.g., NYHA class, ECOG/Karnofsky, relapse definitions, prior-line therapy, inadequate response).
- Boolean logic (any/all), temporal windows (lookback from index date), and “washout” periods were not consistently encoded, causing over- or under-estimation of eligible patients.
- Slow, imbalanced enrollment
- Historic trials saw prolonged FPI-to-LSLV due to poor pre-screen yield and high screen failure rates; recruitment burden fell disproportionately on a few sites, while long-tail sites under-enrolled; outreach to community practices and integrated delivery networks lagged.
- Site selection based on relationships rather than data
- Site feasibility decisions relied on investigator reputation and self-reported counts; there was limited use of objective signals such as historical enrollment velocity by indication, EHR coverage of target phenotypes, screen failure history, protocol deviation rates, data completeness, and competing trials.
- Governance and privacy uncertainty
- DSAs/DUAs and IRB pathways for HIPAA-compliant use of limited datasets (LDS) and de-identified data were inconsistent; tokenization/pseudonymization and privacy-preserving record linkage (PPRL) strategies were not standardized; EU/UK data flows required GDPR-compliant controls.
- Model risk and operational fit
- Algorithms were developed as proofs-of-concept without MLOps, bias/fairness testing, or monitoring; outputs were not integrated into CTMS, site workflows, or outreach CRMs; clinicians distrusted black-box criteria inference.
- Protocol feasibility gaps
- Feasibility simulations were not run before protocol finalization; exclusion criteria and visit schedules created avoidable barriers (e.g., rare biomarker+comorbidity combinations, burdensome imaging windows) that reduced eligible pool size.
Observed signals included: repeated site activations with minimal enrollment; high pre-screen failure due to missing labs or unrecognized contraindications; low conversion from outreach to screening at community sites; feasibility estimates that diverged materially from realized accrual; and hesitant IRB/Privacy approval cycles caused by unclear data flows. Leadership requested a validated, privacy-preserving RWE and clinical AI program with measurable improvements in time-to-enroll, screen-fail reduction, and site productivity.
2) Project Objective
The primary objective focused on designing and deploying RWE pipelines and clinical AI—patient finding, eligibility inference, and site selection—to accelerate enrollment, reduce screen failures, and improve protocol feasibility, using privacy-preserving data integration, computable phenotypes, and model governance that met HIPAA/GDPR and IRB expectations.
Secondary objectives included:
- Standing up a standards-based data backbone (OMOP/FHIR) with de-identification/tokenization and PPRL to unify EHR, claims, labs, imaging, genomics, and SDoH feeds.
- Authoring computable eligibility criteria and phenotype libraries using controlled vocabularies (ICD-10, SNOMED CT, LOINC, RxNorm, CPT/HCPCS) and NLP for unstructured notes to infer performance status, disease activity, and prior line-of-therapy.
- Developing site selection models and dashboards that rank sites on enrollment velocity, eligible patient density, data completeness, operational reliability, and competing-trial pressure.
- Integrating outputs into CTMS/CRM and site workflows—pre-screen lists, eligibility scores, and outreach call-downs—with auditable rationale to support investigator trust.
- Implementing MLOps and model risk management: drift/bias monitoring, fairness assessments, model cards, and human-in-the-loop review.
- Embedding feasibility simulation to optimize inclusion/exclusion and visit schedules before protocol lock; quantifying trade-offs in eligible pool vs. scientific rigor.
3) Methodology and Approach
Workstream 1: Data Partnerships, Privacy, and Governance
We established compliant data flows with partners and standardized governance.
- Executed DSAs/DUAs with health systems, networks, and data partners; defined HIPAA pathways (Expert Determination or Safe Harbor), GDPR lawful bases, and limited datasets with PPRL/tokenization; aligned on BAAs/DPAs and cross-border transfer rules.
- Deployed a privacy-by-design architecture: de-identification, token services, consent management where applicable, and role-based access for sponsor, CRO, and site users.
- Established a data council with Privacy/IRB and Clinical Ops for ongoing oversight.
Workstream 2: Data Model and Ingestion (OMOP/FHIR)
We built a harmonized, interoperable data layer for analytics and cohort discovery.
- Mapped EHR feeds (diagnoses, labs, meds, procedures, vitals), claims (diagnosis/procedure, pharmacy, utilization), and registries to OMOP CDM; exposed HL7 FHIR APIs for near-real-time cohort queries; standardized vocabularies and value sets.
- Integrated labs, imaging metadata, genomics summaries, and SDoH (e.g., Area Deprivation Index, distance-to-site) with quality checks and data observability dashboards.
- Implemented federated and centralized configurations as required: onsite compute where data residency mandated, and privacy-preserving cohort counts where direct data sharing was limited.
Workstream 3: Eligibility Parsing and Computable Phenotypes
We translated protocol language into machine-executable criteria and reusable phenotype modules.
- Extracted inclusion/exclusion criteria using NLP and LLM-assisted parsing with human verification; normalized to controlled vocabularies and encoded temporal logic (lookbacks, washouts, “any/all” clauses, numeric thresholds with units).
- Developed phenotype modules: disease severity/activity, prior therapy exposure and response, contraindications, performance status (ECOG/Karnofsky), organ function baselines, and biomarker positivity.
- Established a phenotype library with versioning and traceability—criteria-to-code mappings with validation cohorts and sensitivity/specificity metrics; integrated value set governance.
Workstream 4: NLP for Unstructured Notes and Imaging Reports
We unlocked critical context from clinician notes.
- Deployed NLP pipelines (section detection, negation, temporality) to extract disease activity (e.g., Mayo/EDSS mentions), adverse history, performance scores, contraindications, and prior-line therapy details from notes and imaging narratives.
- Performed expert annotation and inter-rater reliability checks; calibrated entity and relation extraction with bias and error analyses by site and demographic strata.
- Linked NLP outputs to OMOP feature tables with provenance for audit and reproducibility.
Workstream 5: Patient Finding and Eligibility Inference Models
We scored patients for outreach and screening with transparent models.
- Constructed supervised and rules-hybrid models to predict “likely eligible now” vs. “eligible with minimal additional work-up,” using structured features, NLP-derived concepts, and temporal patterns; produced explanations and missing-data flags (e.g., “needs lab X within Y days”).
- Benchmarked precision/recall against manual pre-screen logs; targeted high-precision lists to minimize site burden; configured thresholds per site capacity.
- Enabled cohort discovery dashboards (query builder + phenotype library) for protocol and country-level feasibility.
Workstream 6: Site Selection and Performance Analytics
We ranked sites using objective evidence and simulation.
- Built site scores from historical enrollment velocity by indication, screen-failure rates (with reasons), protocol deviation rates, data completeness, EHR coverage of target phenotypes, investigator workload, competing-trial density, and distance-to-site for eligible patients.
- Simulated enrollment under different site mixes and activation sequences; recommended portfolio of core and satellite sites (including community practices) with expected monthly accrual and monitoring burden.
- Delivered site playbooks: phenotype counts, outreach guidance, referral network insights, and expected lab/imaging gaps.
Workstream 7: Feasibility Simulation and Protocol Optimization
We quantified eligibility and burden trade-offs before protocol lock.
- Ran scenario models to estimate eligible pool size and accrual speed under alternate inclusion/exclusion combinations and visit schedules; highlighted criteria driving screen failures.
- Presented protocol modification options (e.g., lab windows, stable comorbidity allowances) with impact on statistical power and operational complexity.
Workstream 8: Integration with CTMS/EDC/IRT and Site Workflows
We embedded outputs where work happens.
- Fed pre-screen lists and eligibility scores to CTMS and site CRMs; exposed query APIs for site pre-screen modules; configured alerts for new potential matches based on EHR refreshes.
- Tracked downstream conversion (contacted → consented → screened → randomized) to calibrate models continuously and measure ROI.
Workstream 9: MLOps, Bias/Fairness, and Model Governance
We operationalized models with guardrails.
- Instituted model registry, versioning, and CI/CD; monitored data drift, calibration, precision/recall, and site-specific performance; retrained models per approved SOPs.
- Assessed fairness across protected groups; documented mitigations (reweighing, threshold tuning) and set governance thresholds; produced model cards with intended use, limitations, and validation evidence.
Workstream 10: Change Management and Site/IRB Enablement
We prepared clinical teams and sites for adoption.
- Developed training for Clinical Ops, sites, and CRA teams on phenotype definitions, score interpretation, and outreach best practices; created IRB packets explaining data flows, privacy controls, and recruitment processes.
- Set cadence for feasibility reviews and enrollment stand-ups; aligned incentives and SLAs for outreach response and data quality.
4) Data Request
We requested datasets and documentation to execute RWE and clinical AI for trial acceleration across clients in the therapeutics spectrum. Typical horizons: 24–36 months retrospective EHR/claims; monthly/weekly refreshes; and site performance from the last 3–5 trials per indication.
- Governance and privacy:
- DSAs/DUAs/BAAs/DPAs; IRB protocols and consent templates; de-identification and tokenization specifications; data retention and access policies; cross-border data transfer constraints.
- EHR/clinical:
- Structured EHR extracts (diagnoses, medications, procedures, labs, vitals), encounter context; unstructured notes and imaging reports (secured for NLP); mapping tables to OMOP and FHIR resources; coding dictionaries (ICD-10, SNOMED CT, LOINC, RxNorm, CPT/HCPCS).
- Claims and registries:
- Medical and pharmacy claims with enrollment files; disease registries; central lab/genomics summaries with identifiers for PPRL.
- Site performance:
- Historical enrollment by site/indication, pre-screen logs, screen failure reasons, protocol deviations, data completeness scores, time-to-activation, competing trials by site.
- Study operations:
- CTMS/EDC/IRT schemas; outreach CRM logs; country-specific startup timelines; monitoring burden metrics.
- SDoH and geography:
- Deprivation indices, payer mix proxies, and travel time to sites from patient clusters; language and access barriers where available.
Common pitfalls included incomplete lab result coding, inconsistent encounter times, limited note availability for NLP, broken identifiers between EHR and claims, varying data latency by partner, non-standard site naming, and missing screen failure reasons. We mitigated with OMOP/FHIR mapping checks, tokenization/PPRL reconciliation, data observability dashboards, and a data dictionary with domain stewardship.
5) Questions for Client
- Which protocol(s) and geographies are priority for enrollment acceleration in the next 6–12 months, and what milestones (FPI/LSLV/DBL) are time-critical?
- What eligibility criteria are most contentious or likely to drive screen failures; what flexibility exists for protocol amendments?
- Which data partners and health systems can be onboarded quickly; what DSAs/DUAs and IRB pathways are feasible?
- What deployment model is acceptable (centralized vs. federated vs. hybrid), and what tokenization/PPRL approach meets privacy constraints?
- What precision/recall targets and site-burden limits should govern patient-finding lists (e.g., max pre-screens per randomization)?
- Which CTMS/CRM integrations are required for operational adoption; who owns outreach workflows at sites?
- What fairness and representativeness constraints must be evaluated (by age, sex, race/ethnicity, payer type, geography)?
- How will Clinical Ops and Medical monitor and approve phenotype logic changes; what governance cadence is expected?
- What site selection criteria are mandatory (e.g., historic velocity, eligible patient density, data quality), and how should we weight them?
- What success metrics must be proven in pilot (time-to-consent reduction, screen-fail reduction, enrollment rate uplift) to justify scale?
6) Interview Guide for Subject Matter Experts
Chief Medical Officer / Clinical Development Lead
- Which criteria are essential for scientific validity and safety; where is flexibility acceptable without compromising endpoints?
- What historic screen-fail patterns have you observed; which criteria or procedures were most burdensome?
- How will enrollment acceleration alter country/site strategy or evidence plans?
Head of Clinical Operations
- Where do pre-screen and screening workflows break; how are sites currently prioritized and supported?
- Which CTMS/CRM processes must ingest patient-finding outputs; what SLAs should govern follow-up?
- How do you measure site productivity and when do you intervene?
RWE / Data Science Lead
- What EHR/claims sources and mappings (OMOP/FHIR) exist today; where are the biggest gaps?
- How should we balance rules-based vs. ML models; what thresholding strategies fit operational realities?
- What evaluation datasets can be used to benchmark precision/recall and drift?
Privacy / Compliance / IRB Liaison
- What de-identification and tokenization approaches are acceptable; how should DSAs/DUAs be structured?
- Which IRB models (central vs. local) and consent frameworks apply to patient outreach vs. LDS analytics?
- What governance artifacts are required for audits and inspections?
Site & Investigator Relations
- What information do sites need to trust pre-screen lists; how should we present rationale and missing-data flags?
- Which sites excel at outreach; where is additional support (nurse navigators, community outreach) needed?
Biostatistics
- How will feasibility changes affect power and stratification; what scenarios are acceptable?
- What synthetic control or external comparator opportunities exist using RWE for supportive analyses?
Regulatory Affairs
- What expectations do regulators have for RWE use in feasibility and protocol amendments; how should we document methods and bias mitigations?
- Where have prior submissions faced questions on representativeness or selection bias?
IT / CTMS / EDC Owner
- What integration options (APIs, data pushes) exist for CTMS/CRM; what identity and access controls apply?
- How will audit trails and data lineage be maintained for pre-screen actions?
7) Timeline
We executed a 12–14 week plan with decision gates tailored to RWE And Clinical AI For Trial Acceleration within AI, Data & Analytics.
- Weeks 1–2: Diagnostic and Governance Setup
- Reviewed protocols; analyzed historic enrollment and screen failures; inventoried data partners and DSAs/DUAs; defined privacy/tokenization plan and governance.
- Decision Gate A: Approved priority indications/geographies, privacy architecture (centralized vs. federated/hybrid), evaluation metrics, and site cohort for pilots.
- Weeks 3–4: Data Model and Ingestion
- Mapped EHR/claims to OMOP/FHIR; set up PPRL/tokenization; onboarded labs/genomics/SDoH; implemented quality checks and observability.
- Decision Gate B: Validated data readiness and coverage; confirmed initial partner participation and refresh cadence.
- Weeks 5–6: Eligibility Parsing and Phenotype Library
- Encoded inclusion/exclusion into computable phenotypes; developed NLP pipelines for unstructured criteria; validated against chart review samples.
- Decision Gate C: Met precision/recall thresholds for key criteria; locked phenotype library v1 and value sets.
- Weeks 7–8: Patient Finding and Site Selection Models
- Trained eligibility inference models; built site selection scores and dashboards; simulated enrollment scenarios; generated pre-screen lists with explanations.
- Decision Gate D: Achieved performance targets; selected pilot sites and outreach thresholds.
- Weeks 9–10: Integration and Pilot Launch
- Integrated with CTMS/CRM; deployed site dashboards; trained site staff; initiated outreach and tracked funnel conversion and workload.
- Decision Gate E: Confirmed adoption and operational KPIs; tuned thresholds and site mixes.
- Weeks 11–12: Monitoring, Fairness, and Protocol Optimization
- Monitored model drift and fairness; delivered feasibility simulation results for potential protocol refinements; documented methods for governance and auditor review.
- Decision Gate F: Approved scale roadmap (additional sites/regions, protocol adjustments) and quarterly governance cadence.
- Weeks 13–14 (optional): Scale and Handoff
- Expanded partner network; standardized DSAs/DUAs; operationalized MLOps dashboards and SOPs; handed off phenotype library stewardship and site analytics playbooks.
Critical path items included DSA/DUA execution and IRB alignment, OMOP/FHIR mapping quality, availability of unstructured notes for NLP, CTMS/CRM integration bandwidth, site training capacity, and governance sign-off on model risk and fairness.
8) Deliverables
- Data and Privacy Architecture
- OMOP/FHIR data model, tokenization/PPRL design, de-identification approach, data flow diagrams, DSAs/DUAs templates, and governance charter.
- Computable Eligibility and Phenotype Library
- Encoded inclusion/exclusion criteria with value sets and temporal logic; phenotype modules with validation metrics, versioning, and traceability.
- NLP and Feature Pipelines
- Note and imaging report extraction with provenance; feature tables integrated with OMOP; error/bias analyses and annotation guidelines.
- Patient Finding Models and Cohort Discovery Portal
- Eligibility inference models with explanations and missing-data flags; cohort query UI; pre-screen list generation and export to CTMS/CRM.
- Site Selection and Feasibility Dashboards
- Site scores (eligibles, velocity, quality, deviations, competing trials), enrollment simulations, and activation plans; site playbooks with outreach guidance.
- Protocol Feasibility Simulation Pack
- Scenario analyses of inclusion/exclusion and visit schedules; expected accrual impacts; recommendations and decision memos for protocol amendments.
- MLOps and Model Governance Toolkit
- Model registry, monitoring dashboards, drift/fairness reports, model cards, SOPs for retraining, and change-control templates.
- Integration and Adoption Guides
- CTMS/CRM integration specs; site onboarding and training materials; IRB explainer packets; operational SOPs for outreach and pre-screen workflows.
- KPI and Economics Dashboard
- Time-to-consent, screen-fail rate, enrollment velocity, site workload, threshold tuning impact; investment case for scale.
- Governance and Compliance Binder
- Privacy impact assessments, IRB approvals, DSA/DUA summaries, phenotype and model validation reports, and audit-ready documentation.
9) Industry Insights
- Interoperability drives speed:
- OMOP and FHIR enable faster onboarding of new EHR partners and reduce bespoke ETL maintenance; tokenization/PPRL is becoming the norm for multi-source linkage under HIPAA/GDPR constraints.
- From narrative to computable:
- Parsing and encoding eligibility with value sets and temporal logic—augmented by NLP for unstructured criteria—turns feasibility and recruitment into an operational pipeline; libraries pay dividends across trials and amendments.
- Precision over volume at sites:
- High-precision pre-screen lists build investigator trust and conserve coordinator time; explainability and “what’s missing” flags increase conversion and reduce screen failures.
- Site selection must be evidence-based:
- Historical enrollment velocity, screen-fail patterns, deviations, and EHR coverage of target phenotypes predict performance better than reputation alone; community sites expand reach when equipped with navigators and streamlined workups.
- Feasibility should be simulated, not guessed:
- Protocol optimization via scenario modeling prevents avoidable bottlenecks; modest relaxations or window adjustments can materially expand the eligible pool without compromising rigor.
- Governance and fairness are not optional:
- Bias and representativeness must be measured and mitigated; regulators increasingly scrutinize selection processes and demographic distribution; model cards and SOPs reduce audit friction.
- Hybrid deployment models are practical:
- Federated analytics and privacy-preserving cohort counts enable participation by conservative health systems; centralized models can be calibrated with federated features where needed.
- What “good” looks like:
- Privacy-preserving RWE pipelines; computable eligibility libraries; NLP-enhanced features; patient-finding with explanations; evidence-based site selection; CTMS/CRM integration; feasibility simulation; MLOps with drift/fairness monitoring; and governance that satisfies IRB and privacy requirements—demonstrably improving enrollment velocity and reducing screen fails.
- Near-term watch points:
- IRB capacity and variability; EHR note access for NLP; tokenization vendor interoperability; protocol amendments causing model drift; hospital IT bandwidth; and evolving privacy regulations. Quarterly governance reviews and continuous evaluation sustain value and compliance.
Implications for clients we served included enabling Emerging Therapeutic Biotech to ground feasibility in real data before IND; supporting Clinical-Stage Biotech to cut time-to-enroll and screen-fail rates via precise patient finding and site selection; equipping Commercial Biotech to accelerate post-approval studies with community-site reach; guiding Platform Biotech to standardize phenotype libraries and RWE pipelines across partnered programs; and giving Biotech Investors & Incubators diligence lenses on enrollment risk, data partner readiness, and AI governance maturity in portfolio companies.
